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Angela Meyer

Assistant Professor at TU Delft and BFH

Bern University of Applied Sciences

School of Engineering and Computer Science

Quellgasse 21, CH-2501 Biel

angela.meyer@bfh.ch

Phone: +41 32 321 64 69   Website

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Delft University of Technology

Department of Geoscience and Remote Sensing

Stevinweg 1, NL-2628 CN Delft

angela.meyer@tudelft.nl

Phone: +31 15 278 8392   Website

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We are hiring!

Our group has 5 new PhD positions and a postdoc position coming up in new projects in data assimilation, emulation, and forecasting for energy applications. Drop me a message with your CV, graduation certificates and references if you are interested. 

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Research

I am an Assistant Professor of Energy Meteorology at TU Delft and a Professor of Applied Machine Learning at BFH. My research aims at developing intelligent decision support systems to increase the resilience and sustainability of industrial and energy systems with sensor-driven and machine learning approaches. Before joining BFH, I was a doctoral and postdoctoral researcher at ETH Zurich, developed machine learning and predictive maintenance applications at the R&D centre of Hexagon AB, and led a remote condition monitoring R&D program at Siemens Smart Infrastructure. Our research group is supported by the European Commission, the Swiss Innovation Agency Innosuisse and National Science Foundation.

News

Sept 3, 2024: Our study A. Marza, D. Domeisen, A. Meyer, Machine learning-driven assessment and prediction of sub-seasonal forecast skill, and invited talk A. Meyer, A. Carpentieri, K. Schuurman, Probabilistic minute-scale forecasting of solar energy across Europe will be presented at EMS 2024 in September [Link][Link]

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May 15, 2024: Accepted! Our paper A. Grataloup, S. Jonas, A. Meyer, A review of federated learning in renewable energy applications: Potential, challenges, and future directions has been accepted for publication in Energy and AI. [Link]

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Mar 14, 2024: Our study K. Schuurman, A. Meyer, Predicting surface solar irradiance from satellite imagery with deep learning radiative transfer emulation, will be presented at EGU24 in Vienna in April [Link], and so will be our paper Carpentieri, A., D. Folini, J. Leinonen, A. Meyer, SHADECast: Enhancing solar energy integration through probabilistic regional forecasts [Link]

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Dec 22, 2023: Excited to share our new papers: Carpentieri, A., D. Folini, J. Leinonen, A. Meyer, Extending intraday solar forecast horizons with deep generative models, arXiv:2312.11966 [Link], and Grataloup, A., S. Jonas, A. Meyer, A review of federated learning in renewable energy applications: Potential, challenges, and future directions, arXiv:2312.11220 [Link].

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Preprints

  • ​Schuurman, K., A. Meyer, Surface solar radiation: AI satellite retrieval can outperform Heliosat and generalizes to other climate zones, https://arxiv.org/abs/2409.16316 [Link]

  • Grataloup, A., S. Jonas, A. Meyer, 2024, Wind turbine condition monitoring based on intra- and inter-farm federated learning, doi:10.48550/arXiv.2409.03672 [Link]

Publications

  • ​Carpentieri, A., D. Folini, J. Leinonen, A. Meyer, 2024, Extending intraday solar forecast horizons with deep generative models, Applied Energy, doi:10.1016/j.apenergy.2024.124186 [Link] 

  • Grataloup, A., S. Jonas, A. Meyer, 2024, A review of federated learning in renewable energy applications:  Potential, challenges, and future directions, Energy and AI, 17, doi:10.1016/j.egyai.2024.100375 [Link]

  • Jonas, S., K. Winter, B. Brodbeck, A. Meyer, 2024, Bias correction of wind power forecasts with SCADA data and continuous learning, Journal of Physics: Conference Series, doi:10.1088/1742-6596/2767/9/092061 [Link]

  • Bilendo, F., N. Lu, H. Badihi, A. Meyer, U. Cali, P. Cambron, 2024, Multi-Target Normal Behavior Model Based on Heterogeneous Stacked Regressions and Change-Point Detection for Wind Turbine Condition Monitoring, IEEE Transactions on Industrial Informatics, 20, 4, doi:10.1109/TII.2023.3331766 [Link]

  • Gkantou, M., E. Marino, A. Malekjafarian, S. Bali, C. Baniotopoulos, J. van Beeck, R. Borg, N. Bruschi, P. Cardiff, E. Chatzi, I. Cudina, F. Dinu, E. Erhymiou, G. Ferri, H. Gervasio, J. Heng, Z. Jiang, S. Lenci, I. Lukacevic, L. Manuel, A. Meyer, M. Mendez Morales, A. Osmanovic, V. Pakrashi, A. Pandit, G. Rega, D. Skejic, L. Tesch, D. Ungureanu, T. Uzunovic, A. Shankar Vermar, 2024, Offshore renewable energies: a review towards Floating Modular Energy Islands – Monitoring, Loads, Modeling and Control, Ocean Engineering, 313, 119251, doi:10.1016/j.oceaneng.2024.119251 [Link]

  • Carpentieri, A., S. Pulkkinen, D. Nerini, D. Folini, M. Wild, A. Meyer, Intraday probabilistic forecasts of surface solar radiation with cloud scale-dependent autoregressive advection, Applied Energy, 351, 2023. doi: 10.1016/j.apenergy.2023.121775 [Link] [PDF]

  • Jenkel, L., S. Jonas, A. Meyer, Privacy-preserving Fleet-wide Learning of Wind Turbine Conditions with Federated Learning, Energies, 16(17), doi: 10.3390/en16176377, 2023. [Link]

  • Meyer, A., SCADA-based fault detection in wind turbines: Data-driven techniques and applications, In: Non-Destructive Testing and Condition Monitoring Techniques In Wind Energy, Academic Press, Editors: F. Marquez, M. Papaelias, V. Jantara Junior, ISBN 9780323996662, doi: 10.1016/B978-0-323-99666-2.00001-0, 2023. [Link]

  • Carpentieri, A., D. Folini, M. Wild, L. Vuilleumier, A. Meyer, Satellite-derived solar radiation for intra-hour and intra-day applications: Biases and uncertainties by season and altitude, Solar Energy, 255, 274-284, doi: 10.1016/j.solener.2023.03.027, 2023. [Link]

  • Jonas, S., D. Anagnostos, B. Brodbeck, A. Meyer, Vibration fault detection in wind turbines based on normal behaviour models without feature engineering, Energies, 16(4), 1760, doi: 10.3390/en16041760, 2023. [Link]

  • Bilendo, F., A. Meyer, H. Badihi, N. Lu, P. Cambron, B. Jiang, Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms - A Review, Energies, 16(1), 180, doi:10.3390/en16010180, 2022.

  • Meyer, A., Vibration Fault Diagnosis in Wind Turbines based on Automated Feature Learning, Energies, 15(4), doi: 10.3390/en15041514, 2022. [Link]

  • Maron, J., D. Anagnostos, B. Brodbeck, A. Meyer, Artificial intelligence-based condition monitoring and predictive maintenance framework for wind turbines, Journal of Physics Conference Series, doi: 10.1088/1742-6596/2151/1/012007, 2022. [Link]

  • Meyer, A., Multi-target normal behaviour models for wind farm condition monitoring, Applied Energy, doi: 10.1016/j.apenergy.2021.117342, 2021. [Link] [Article]

  • Meyer, A., Early fault detection with multi-target neural networks, Lecture Notes in Computer Science, Vol. 12953, Springer, in: O. Gervasi et al. (Eds.): ICCSA 2021, LNCS 12951, pp. 1–9, 2021, doi: 10.1007/978-3-030-86970-0_30, 2021.

  • Meyer, A., B. Brodbeck, Data-driven Performance Fault Detection in Commercial Wind Turbines, Proceedings of the 5th European Conference of the Prognostics and Health Management Society (PHME20), ISBN 978-1-93-626332-5, 2020. [Download]​

  • Vuilleumier, L.*, A. Meyer*, R. Stöckli, S. Wilbert, L. Zarzalejo, Accuracy of Satellite-derived Solar Direct Irradiance in Southern Spain and Switzerland, International Journal of Remote Sensing, doi: 10.1080/01431161.2020.1783712, 2020. *shared first authorship

  • Kuhn, P., S. Wilbert, C. Prahl, D. Garsche, D. Schüler, T. Haase, L. Ramirez, L. Zarzalejo, A. Meyer, P. Blanc, R. Pitz-Paal, Applications of a shadow camera system for energy meteorology, Advances in Science and Research, doi: 10.5194/asr-15-11-2018, 2018.

  • Kuhn, P., B. Nouri, S. Wilbert, C. Prahl, N. Kozonek, T. Schmidt, Z. Yasser, L. Ramirez, L. Zarzalejo, A. Meyer, L. Vuilleumier, D. Heinemann, P. Blanc, R. Pitz‐Paal, Validation of an all‐sky imager–based nowcasting system for industrial PV plants, Progress in Photovoltaics: Research and Applications, 26, doi: 10.1002/pip.2968, 2018.

  • Kuhn, P., S. Wilbert, C. Prahl, D. Schüler, T. Haase, T. Hirsch, M. Wittmann, L. Ramirez, L. Zarzalejo, A. Meyer, L. Vuilleumier, P. Blanc, R. Pitz-Paal, Shadow camera system for the generation of solar irradiance maps, Solar Energy, doi: 10.1016/j.solener.2017.05.074, 2017.

  • Gasparini, B.*, A. Meyer*, D. Neubauer, S. Münch, U. Lohmann, Cirrus cloud properties as seen by the CALIPSO satellite and ECHAM-HAM global climate model, Journal of Climate, doi: 10.1175/JCLI-D-16-0608.1, 2017. [Article] *shared first authorship

  • Kuhn, P., S. Wilbert, D. Schüler, C. Prahl, T. Haase, L. Ramirez, L. Zarzalejo, A. Meyer, L. Vuilleumier, P. Blanc, J. Dubrana, A. Kazantzidis, M. Schroedter-Homscheidt, T. Hirsch, R. Pitz-Paal, Validation of spatially resolved all sky imager derived DNI nowcasts, AIP Conference Proceedings, doi: 10.1063/1.4984522, 2017.

  • Meyer, A., D. Folini, U. Lohmann, T. Peter, Tropical temperature and precipitation responses to large volcanic eruptions: Observations and AMIP5 simulations, Journal of Climate, doi: 10.1175/JCLI-D-15-0034.1, 2016. [Article]

  • Meyer, A., J.-P. Vernier, B. Luo, U. Lohmann, T. Peter, Did the 2011 Nabro eruption affect the optical properties of ice clouds?, J. Geophys. Res. Atmos., 120, doi: 10.1002/2015JD023326, 2015. [Article]

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